An exemplary quantum computing apparatus includes qubits, which are analogous to bits employed in conventional transistor-based computing devices. While bits are binary in nature, a qubit can have a state associated therewith that is representative of a 1, a 0, or a superposition of a 1 and 0. Multi-qubit systems are further distinguished from classical systems through forming quantum entangled systems. Entanglement and tunneling are resources identified as possible sources for computational acceleration that can lead to exceeding the capabilities of conventional transistor-based computing devices.
Adiabatic quantum annealing has been proposed as a technology for employment in a quantum computing apparatus. Generally, adiabatic quantum annealing refers to representing an optimization problem in terms of an energy landscape for which the global minimum represents the optimized solution. Starting from a known ground state, constructed using an external field that can be modulated to zero, the system is slowly evolved to the ground state of the optimization problem as the external field is turned off. In conventional implementations of adiabatic quantum annealing in quantum computing apparatuses, qubits have been pursued in superconductors, wherein a flux associated with a qubit is indicative of its state (e.g., 1, 0, or a superposition of both). Superconductor-based qubits have been applied to a specific optimization problem, referred to as a quadratic unconstrained binary optimization (QUBO) problem. More particularly, a quantum computing apparatus that comprises 100 superconductor qubits has been developed, and has been shown to accurately solve particular QUBO problems. Scaling properties in this relatively small number of qubits appears promising relative to conventional computer solve times for random samplings of QUBO problems (e.g. not necessarily hard problems).
Various deficiencies corresponding superconductor qubits have been identified, however, including but not limited to 1) a restricted tunability for the adiabatic evolution (e.g., the alteration of energy states); 2) programming precision; 3) energy gap size relative to noise (e.g., at least in the 100 qubits regime); 4) relatively fast noise dynamics near anti-crossings sufficient to produce errors; 5) lack of a clear approach to suppress or correct errors; 6) qubit uniformity and yield; and 7) lack of tests that prove enhanced speed of the quantum computing apparatus relative to other quantum computing apparatuses (e.g., measures of adiabaticity, quantum behavior, and entanglement).
Other exemplary approaches that have been discussed pertaining to adiabatic quantum computing include the history state approach, holonomic gates, and quantum simulation. Additionally, effort has been set forth in atomic physics-based quantum computing to utilize ions and neutrons in connection with performing computation. Currently, however, no known correction for loss of neutrons or ions exists for the adiabatic quantum computing approach, rendering it difficult to implement such an adiabatic quantum computing algorithm without significant error.